Contrasting Trends and Drivers of Global Surface and Canopy Urban Heat Islands

Abstract

A comprehensive comparison of the trends and drivers of global surface and canopy urban heat islands (termed Is and Ic trends, respectively) is critical for better designing urban heat mitigation strategies. However, such a global comparison remains largely absent. Using spatially continuous land surface temperatures and surface air temperatures (2003–2020), here we find that the magnitude of the global mean Is trend (0.19 ± 0.006°C/decade, mean ± SE) for 5,643 cities worldwide is nearly six‐times the corresponding Ic trend (0.03 ± 0.002°C/decade) during the day, while the former (0.06 ± 0.004°C/decade) is double the latter (0.03 ± 0.002°C/decade) at night. Variable importance scores indicate that global daytime Is trend is slightly more controlled by surface property, while background climate plays a more dominant role in regulating global daytime Ic trend. At night, both global Is and Ic trends are mainly controlled by background climate.

Satellite Clear‐Sky Observations Overestimate Surface Urban Heat Islands in Humid Cities

“…This study addresses this research gap by conducting quantitative analysis on a global scale, offering direct evidence for the overestimation of the SUHI effect when relying on the LST observations under clear-sky conditions. More crucially, existing studies investigating the spatiotemporal patterns and driving factors of the SUHI effect primarily rely on clear-sky LST data (Cao et al, 2016;Chakraborty & Lee, 2019;Clinton & Gong, 2013;Du et al, 2023;Gui et al, 2019;Imhoff et al, 2010;Lai et al, 2018bLai et al, , 2021aX. Li et al, 2023;Z.…”

“…This study addresses this research gap by conducting quantitative analysis on a global scale, offering direct evidence for the overestimation of the SUHI effect when relying on the LST observations under clear‐sky conditions. More crucially, existing studies investigating the spatiotemporal patterns and driving factors of the SUHI effect primarily rely on clear‐sky LST data (Cao et al., 2016; Chakraborty & Lee, 2019; Clinton & Gong, 2013; Du et al., 2023; Gui et al., 2019; Imhoff et al., 2010; Lai et al., 2018b, 2021a; X. Li et al., 2023; Y. Liu et al., 2021, 2023; Z. Liu et al., 2022a, 2022b; S. Peng et al., 2012; Quan et al., 2016; Q. Yang et al., 2017, 2019, 2023a; C. Yang & Zhao, 2023; Yao, Wang, Huang, Chen, et al., 2018; Yao et al., 2019; B. Zhou et al., 2013, 2017; D. Zhou et al., 2014, 2016). The findings from these studies could benefit from the updates to integrate the differences in their spatial patterns between clear‐sky and all‐sky SUHIIs.…”

Spatiotemporal Variations of Aerosol Optical Depth and the Spatial Heterogeneity Relationship of Potential Factors Based on the Multi-Scale Geographically Weighted Regression Model in Chinese National-Level Urban Agglomerations

“…The MGWR model not only revealed the direction and intensity of the influence of each factor on AOD, but it also demonstrated the spatial heterogeneity of the response of each factor with AOD using statistically significant drivers. Note that multicollinearity between the variables can distort the results of the assessment of spatial heterogeneity [66,79]. Therefore, using the local linear test, variables with a variance inflation factor (VIF) of <5 were retained (Supplementary Table S2), including eight variables (i.e., PM2.5, DEM, Slope, GDP, PD, NDVI, WS, and RH).…”